Evaluating a blended degree program through the use of the NSSE framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The purpose of this student‐faculty partnership research study was to evaluate the effectiveness of a blended four‐year Bachelor of Education Elementary Program at a Canadian university using the National Survey of Student Engagement (NSSE) framework. Data was collected from the first graduating cohort of students from the B.Ed. program in partnership with four Undergraduate Student Research Assistants (USRA). The students in this study completed online surveys and participated in focus groups at the end of their first and fourth years in the program. The study participants provided recommendations for improving the quality of the program based on the five NSSE benchmarks and the use of digital technologies. The main recommendations that emerged from this study were that student and faculty interactions, outside of the classroom, could be enhanced through the use of web‐based conferencing tools to support “virtual” office hours. Course assignments that incorporate peer mentoring activities through the use of social media applications could provide richer opportunities for active and collaborative learning. Creating more intentional connections between academic coursework and field placements through the use of Google applications could help to strengthen the relationship between theory and practice in the program. Enriching educational experiences could be expanded through the use of social media applications to promote and communicate student led academic and social events. A supportive campus environment could be improved by the development of a digital “road map” and co‐curricular record for the program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it